update output

avallecam · avallecam · commit 99c51ed1476e · 2025-07-25T13:45:44.000-04:00
diff --git a/instructors/04-practical-tutors.qmd b/instructors/04-practical-tutors.qmd
@@ -61,10 +61,10 @@ Generate disease trajectories of **infectious individuals** and **new infections
 
 Within your room, Write your answers to these questions:
 
-- What are the **time** and **size** of the epidemic peak for **infectious individuals** in each age group?
-- Describe any differences between these two outputs: the epidemic peak of *infectious individuals* and the plot of *new infections*.
-- Modify the basic reproduction number (R₀) from 1.46 to 1.1 or 3. What changes do you observe in the **time** and **size** of the peak of *new infections*?
-- **Compare**: What differences do you notice compared to the outputs from other rooms (if available)?
+- What are the time and size of the epidemic peak for *infectious individuals* in each age group?
+- Compare and describe the similarities and differences between these two outputs: the epidemic peak of *infectious individuals* and the plot of *new infections*.
+- Modify the basic reproduction number (R₀) from 1.46 to 1.1 or 3. What changes do you observe in the time and size of the peak of *new infections*?
+- Compare: What differences do you notice compared to the outputs from other rooms (if available)?
 
 ### Inputs
 
@@ -186,16 +186,16 @@ epidemics::epidemic_peak(data = simulate_baseline)
 
 #### Interpretation
 
-Interpret the **time** and **size** of peak for infectious individuals:
+Interpretation template:
 
 + In the population, the demographic group of `age from [0,20]` has a peak of infectious individuals at day 320 with a size of `513,986`.
 
-Differences:
+Compare output types:
 
 + `epidemics::epidemic_peak(data = simulate_baseline)`
-    + The table output gives exact values for time and size of peak for **infectious individuals**.
+    + The table output gives exact values for time and size of peak for *infectious individuals*.
 + `epidemics::new_infections(data = simulate_baseline)`
-    + We can not get exact value for time and size of peak of **new infections**.
+    + We can not get exact value for time and size of peak of *new infections*.
     + We can make qualitative comparisons.
 + Comparing plots:
     + The peak size of *new infections* is lower than the peak size of *infectious individuals*.
@@ -384,7 +384,7 @@ Interpretation Helpers:
 
 
 
-## Activity 3: Combine interventions
+## Activity 3: Combine interventions (Optional)
 
 Combine two intervention in the same simulation and compare the disease trajectories of new infections against the baseline or only one intervention. Use the intervention parameters above.
 
diff --git a/instructors/files/04-practical-tutors.md b/instructors/files/04-practical-tutors.md
@@ -20,10 +20,10 @@ This practical is based in the following tutorial episodes:
 
 This practical has three activities.
 
-## Activity 1: Generate disease trajectories of new infections
+## Activity 1: Generate disease trajectories
 
-Generate disease trajectories of infectious subjects and new infections
-using the following available inputs:
+Generate disease trajectories of **infectious individuals** and **new
+infections** using the following available inputs:
 
 - Social contact matrix
 - Age group of the infectious population
@@ -32,16 +32,16 @@ using the following available inputs:
 
 Within your room, Write your answers to these questions:
 
-- What is the location (time) and size of epidemic peak for infectious
-  subjects in each age group?
-- What is the number of new infections at the epidemic peak?
-- Change the basic reproduction number to 1.1 and 3. Are the changes in
-  location (time) and size of the peak of new infections as expected?
-  (based on the concept definition of reproduction number)
-- Interpret: How would you communicate these results to a
-  decision-maker?
-- Compare: What differences do you identify from other room outputs? (if
-  available)
+- What are the time and size of the epidemic peak for *infectious
+  individuals* in each age group?
+- Compare and describe the similarities and differences between these
+  two outputs: the epidemic peak of *infectious individuals* and the
+  plot of *new infections*.
+- Modify the basic reproduction number (R₀) from 1.46 to 1.1 or 3. What
+  changes do you observe in the time and size of the peak of *new
+  infections*?
+- Compare: What differences do you notice compared to the outputs from
+  other rooms (if available)?
 
 ### Inputs
 
@@ -54,7 +54,7 @@ Within your room, Write your answers to these questions:
 | Parameter | Value | Notes |
 |----|----|----|
 | Age Limits | 0, 20, 40 | Age group cutoffs |
-| Infectious Population | 1 / 1,000,000 | 1 infectious individual per million people |
+| Infectious Population | 1 / 1,000,000 | 1 infectious individual per million people in Age group 20-40 |
 | Basic Reproduction Number | 1.46 | R₀ value for influenza |
 | Pre-infectious Period | 3 days | Incubation before becoming infectious |
 | Infectious Period | 7 days | Duration of infectiousness |
@@ -84,18 +84,36 @@ Within your room, Write your answers to these questions:
 Interpretation template:
 
 - In the population, the demographic group of `age from [0,20]` has a
-  peak of infectious subjects at day 320 with a size of `513,986`.
-
-Interpretation Helpers:
+  peak of infectious individuals at day 320 with a size of `513,986`.
+
+Compare output types:
+
+- `epidemics::epidemic_peak(data = simulate_baseline)`
+  - The table output gives exact values for time and size of peak for
+    *infectious individuals*.
+- `epidemics::new_infections(data = simulate_baseline)`
+  - We can not get exact value for time and size of peak of *new
+    infections*.
+  - We can make qualitative comparisons.
+- Comparing plots:
+  - The peak size of *new infections* is lower than the peak size of
+    *infectious individuals*.
+  - Both plot trajectories seem to share the time of peak.
+  - Other packages that can estimate trend of new infections are
+    `{EpiNow2}` and `{epichains}`.
+
+Modify the basic reproduction number (R₀):
 
 | R = 1.1 | R = 3 |
 |----|----|
 | ![image](https://hackmd.io/_uploads/H1UupFOAyl.png) | ![image](https://hackmd.io/_uploads/ryVoat_R1l.png) |
 
-- An epidemic with R=1.1 has a days delayed and smaller outbreak based
-  on number of infections (day 1200, 9000 new infections), compared with
-  R=3 with a earlier and higher peak than R = 1.5 (day 100, 1,000,000
-  new infections).
+- An epidemic with `R₀ = 1.1` has a days delayed and smaller outbreak
+  based on number of infections (day 1200, 9000 new infections),
+  compared with `R₀ = 3` with an earlier and higher peak than
+  `R₀ = 1.46` (day 100, 1,000,000 new infections).
+
+Comparison between rooms:
 
 | Vietnam | Zimbabwe |
 |----|----|
@@ -104,9 +122,35 @@ Interpretation Helpers:
 - Population structure from Italy, Vietnam, and Zimbabwe influences the
   progression of the transmission in each population.
 
+<!-- -->
+
+    # Italy
+    contact_data$demography
+    #>    age.group population proportion  year
+    #>       <char>      <num>      <num> <int>
+    #> 1:    [0,20)   11204261  0.1905212  2005
+    #> 2:   [20,40)   16305622  0.2772665  2005
+    #> 3:       40+   31298598  0.5322123  2005
+
+    # Vietnam
+    contact_data$demography
+    #>    age.group population proportion  year
+    #>       <char>      <num>      <num> <int>
+    #> 1:    [0,20)   31847968  0.3777536  2005
+    #> 2:   [20,40)   28759380  0.3411194  2005
+    #> 3:       40+   23701489  0.2811270  2005
+
+    # Zimbabwe
+    contact_data$demography
+    #>    age.group population proportion  year
+    #>       <char>      <num>      <num> <int>
+    #> 1:    [0,20)    8235388  0.5219721  2015
+    #> 2:   [20,40)    5179150  0.3282628  2015
+    #> 3:       40+    2362911  0.1497651  2015
+
 ## Activity 2: Compare interventions
 
-Compare the disease trajectories of new infections against an
+Compare the disease trajectories of **new infections** against an
 intervention using the following available inputs:
 
 - Time to start the intervention
@@ -116,33 +160,38 @@ intervention using the following available inputs:
 
 Within your room, write your answers to these questions:
 
-- How does the time to start of the intervention (early/late) impact the
-  timing and size of the peak of new infectious individuals?
-- Is the observed impact of the intervention in these results expected?
-- Interpret: How would you communicate these results to a
+- How can the start of interventions (early vs. late) affect the timing
+  and size of the peak of new infections?
+- Is the impact of the intervention, as shown in these results, what you
+  would expect? Why or why not?
+- Interpret the results: How would you explain these findings to a
   decision-maker?
-- Compare: What differences do you identify from other room outputs? (if
-  available)
+- Compare: What differences do you observe compared to the outputs from
+  other rooms (if available)?
 
 ### Inputs
 
-| Room | Intervention | Early Start | Late Start | Duration (days) | Effect (Reduction or Rate) |
+| Room  | Country  | Survey Link                              |
+|-------|----------|------------------------------------------|
+| 1,2,3 | Zimbabwe | <https://doi.org/10.5281/zenodo.3886638> |
+
+| Room | Intervention | Reduction/Vaccination rate | Early start (day) | Late start (day) | Duration (days) |
 |----|----|----|----|----|----|
-| 1 | School | 100 | 200 | 100 | Age 0–19: 0.5; Age 20+: 0.01 |
-| 2 | Mask | 100 | 200 | 200 | All ages: 0.163 |
-| 3 | Vaccine | 100 | 200 | 150 | All ages: 0.001 |
+| 1 | School | Age 0–19: 0.5; Age 20+: 0.01 | 100 | 200 | 250 |
+| 2 | Mask | All ages: 0.163 | 100 | 200 | 250 |
+| 3 | Vaccination | All ages: 0.001 | 100 | 200 | 250 |
 
 ### Solution
 
 <!-- visible for instructors and learners after practical (solutions) -->
 
 #### Outputs
 
-| Intervention | Early start | Late start |
+| Intervention | Early start (day 100) | Late start (day 200) |
 |----|----|----|
-| **School Closure** | ![image](https://hackmd.io/_uploads/H1IKxq_A1e.png) | ![image](https://hackmd.io/_uploads/rJt8ttdA1e.png) |
-| **Mask Mandate** | ![image](https://hackmd.io/_uploads/rkV2ttO0yx.png) | ![image](https://hackmd.io/_uploads/Bk8wKYOR1l.png) |
-| **Vaccination** | ![image](https://hackmd.io/_uploads/rycdKYOA1e.png) | ![image](https://hackmd.io/_uploads/B1ff-9_Ckl.png) |
+| **School Closure** | ![image](https://hackmd.io/_uploads/HyxrKNWDee.png) | ![image](https://hackmd.io/_uploads/B18MFNWPll.png) |
+| **Mask Mandate** | ![image](https://hackmd.io/_uploads/Hym4FEbDxl.png) | ![image](https://hackmd.io/_uploads/HknbYEbPlg.png) |
+| **Vaccination** | ![image](https://hackmd.io/_uploads/r12Xt4-Dll.png) | ![image](https://hackmd.io/_uploads/Hy6xKN-vex.png) |
 
 #### Interpretation
 
@@ -157,7 +206,7 @@ Interpretation Helpers:
   effectiveness of vaccination can depend on various factors, including
   vaccine efficacy and timing relative to the outbreak.
 
-## Activity 3: Combine interventions
+## Activity 3: Combine interventions (Optional)
 
 Combine two intervention in the same simulation and compare the disease
 trajectories of new infections against the baseline or only one
@@ -197,7 +246,9 @@ Interpretation Helpers:
 - Vaccination can sustain a reduced epidemic peak compared with mask
   mandate alone.
 
-#### Code
+## Code
+
+### Actiivty 1
 
 ``` r
 # nolint start
@@ -345,7 +396,6 @@ simulate_baseline %>%
   )
 
 epidemics::epidemic_peak(data = simulate_baseline)
-epidemics::epidemic_size(data = simulate_baseline)
 
 
 # Plot new infections ----------------------------------------------------
@@ -365,6 +415,8 @@ newinfections_bygroup %>%
 # nolint end
 ```
 
+### Actiivty 2
+
 ``` r
 # nolint start
 
@@ -376,13 +428,8 @@ room_number <- 1 #valid for all
 # Group parameters -------------------------------------------------------
 
 # activity 2/3
-# school_begin_early <- 100
-school_begin_late <- 200
-# mask_begin_early <- 100
-mask_begin_late <- 200
-vaccine_begin_early <- 100
-# vaccine_begin_late <- 200
-
+intervention_begin <- 200 # change
+intervention_duration <- 250 # change
 
 # Intervention 1 ---------------------------------------------------------
 
@@ -395,8 +442,8 @@ rownames(socialcontact_matrix)
 test_intervention <- epidemics::intervention(
   name = "School closure",
   type = "contacts",
-  time_begin = school_begin_late,
-  time_end = school_begin_late + 100,
+  time_begin = intervention_begin,
+  time_end = intervention_begin + intervention_duration,
   reduction = matrix(c(0.5, 0.01, 0.01))
 )
 
@@ -433,7 +480,7 @@ infections_intervention <- epidemics::new_infections(
 
 # Assign scenario names
 infections_baseline$scenario <- "Baseline"
-infections_intervention$scenario <- "School closure" #<COMPLETE>
+infections_intervention$scenario <- "School closure"
 
 # Combine the data from both scenarios
 infections_baseline_intervention <- bind_rows(infections_baseline, infections_intervention)
@@ -442,7 +489,7 @@ infections_baseline_intervention %>%
   ggplot(aes(x = time, y = new_infections, colour = scenario)) +
   geom_line() +
   geom_vline(
-    xintercept = c(simulate_intervention$time_begin, simulate_intervention$time_end),
+    xintercept = c(test_intervention$time_begin, test_intervention$time_end),
     linetype = "dashed",
     linewidth = 0.2
   ) +
@@ -464,8 +511,8 @@ rownames(socialcontact_matrix)
 test_intervention <- epidemics::intervention(
   name = "mask mandate",
   type = "rate",
-  time_begin = mask_begin_late,
-  time_end = mask_begin_late + 200,
+  time_begin = intervention_begin,
+  time_end = intervention_begin + intervention_duration,
   reduction = 0.163
 )
 
@@ -502,7 +549,7 @@ infections_intervention <- epidemics::new_infections(
 
 # Assign scenario names
 infections_baseline$scenario <- "Baseline"
-infections_intervention$scenario <- "Mask mandate" #<COMPLETE>
+infections_intervention$scenario <- "Mask mandate"
 
 # Combine the data from both scenarios
 infections_baseline_intervention <- bind_rows(infections_baseline, infections_intervention)
@@ -511,7 +558,7 @@ infections_baseline_intervention %>%
   ggplot(aes(x = time, y = new_infections, colour = scenario)) +
   geom_line() +
   geom_vline(
-    xintercept = c(simulate_intervention$time_begin, simulate_intervention$time_end),
+    xintercept = c(test_intervention$time_begin, test_intervention$time_end),
     linetype = "dashed",
     linewidth = 0.2
   ) +
@@ -531,8 +578,8 @@ rownames(socialcontact_matrix)
 
 test_intervention <- epidemics::vaccination(
   name = "vaccinate all",
-  time_begin = matrix(vaccine_begin_early, nrow(socialcontact_matrix)),
-  time_end = matrix(vaccine_begin_early + 150, nrow(socialcontact_matrix)),
+  time_begin = matrix(intervention_begin, nrow(socialcontact_matrix)),
+  time_end = matrix(intervention_begin + intervention_duration, nrow(socialcontact_matrix)),
   nu = matrix(c(0.001, 0.001, 0.001))
 )
 
@@ -569,7 +616,7 @@ infections_intervention <- epidemics::new_infections(
 
 # Assign scenario names
 infections_baseline$scenario <- "Baseline"
-infections_intervention$scenario <- "Mask mandate" #<COMPLETE>
+infections_intervention$scenario <- "Vaccination"
 
 # Combine the data from both scenarios
 infections_baseline_intervention <- bind_rows(infections_baseline, infections_intervention)
@@ -578,7 +625,7 @@ infections_baseline_intervention %>%
   ggplot(aes(x = time, y = new_infections, colour = scenario)) +
   geom_line() +
   geom_vline(
-    xintercept = c(simulate_intervention$time_begin, simulate_intervention$time_end),
+    xintercept = c(test_intervention$time_begin, test_intervention$time_end),
     linetype = "dashed",
     linewidth = 0.2
   ) +
@@ -590,6 +637,8 @@ intervention_vaccinate <- test_intervention
 # nolint end
 ```
 
+### Actiivty 3
+
 ``` r
 # nolint start
 
@@ -629,7 +678,7 @@ infections_twointerventions <- epidemics::new_infections(
 
 # Assign scenario names
 infections_baseline$scenario <- "Baseline"
-infections_twointerventions$scenario <- "Mask mandate + School closure" #<COMPLETE>
+infections_twointerventions$scenario <- "Mask mandate + School closure"
 
 # Combine the data from both scenarios
 infections_baseline_twointerventions <- bind_rows(
@@ -647,7 +696,7 @@ infections_baseline_twointerventions %>%
 
 compare_interventions <- bind_rows(
   infections_baseline,
-  infections_baseline_intervention,
+  infections_baseline_intervention, # varies depending on last one runned
   infections_baseline_twointerventions
 )
 
